The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Data processing systems can be programmed to facilitate analysis of a universe of data items. A universe of data items refers to a large collection of data items. An example of such a universe may be a large collection of market instruments. In many instances, the amount of raw data about data items can be massive and dynamically increasing all the time. Under some approaches, an analyst relies on empirical knowledge and cumbersome spreadsheets to analyze markets and instruments. Under some other approaches, markets and instruments are modeled as multi-variable stochastic, predictive systems. In all these approaches, however, what has been captured is often over-simplified and relatively static. These approaches cannot keep up with new variable and new trends appearing in the markets, and therefore can hardly yield reliable insights for future performance of the markets and instruments. These shortcomings are made worse, as these approaches typically provide little support for a user to flexibly model the markets, to promptly react to new trends, and to timely test new hypotheses.